Example input
This sample input is a portion from a lecture on AI Ethics:
"We've all heard a lot of
talk about "responsible AI". What does that even really mean? No one really ever takes the
time to actually define it. Now, taking a look at
some of the principles that we've gone through so far, most ethical principles or rather not principles
for frameworks, take it to mean either
acting with integrity or clarifying the attribution of responsibility and
legal liability. In fact, this is one
of the most popular, if not most important, AI principles out there, because it's cited by
over 70 percent of the existing frameworks for reasons that are
actually quite obvious. At the end of the day, it
really boils down to who do we blame when the AI messes up?"
Example output
The output will be a full outline divided by key concepts/topics, an overall summary, and a glossary of key terms. Below is a sample for each regarding the sample input:
I. Main Concept: Responsible AI
A. Definition ambiguity
B. Tied to integrity, attribution of responsibility, and legal liability
C. Importance: cited in over 70% of frameworks
Summary:
The transcript dives into the concept of responsible AI, highlighting the ambiguity in its definition and the critical role it plays in AI ethics. The discourse revolves around the accountability gap, with a focus on causality, justice, and reparations.
Glossary:
Responsible AI: A term with varied interpretations, often linked to acting with integrity or assigning responsibility and legal liability.